We then adopt the contrastive learning mechanism of SimSiam [1]} to peform the learning upon \(\mathbf {V}_{\text{SDA}}\) , where the loss function for each single view \(z_{ik}^{(j)}=\mathcal {G}(\mathcal {F}(\tilde{x}_{ik}^{(j)}))\)  is defined as follows to encourage the anchor view \(\tilde{x}_{ik}^{(j)}\)  being grouped up with its corresponding positive views:
\(\mathcal {L}_{\text{vw}}(z_{ik}^{(j)}) = -\sum \limits _{j^{\prime } \ne j} \text{CosineSimilarity}(\mathcal {P}(z_{ik}^{(j)}), \textit {stopgrad}(z_{ik}^{(j^{\prime })}))\) 
